




import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


snake_case__			:    Any			     =      logging.get_logger(__name__)


def     lowercase		(  _lowerCAmelCase   ):
  UpperCAmelCase__         = R"""\w+[.]\d+"""
  UpperCAmelCase__         = re.findall(UpperCamelCase__ ,  UpperCamelCase__   )
  for pat in pats:
    UpperCAmelCase__         = key.replace(UpperCamelCase__ ,  """_""".join(pat.split("""."""   )   )   )
  return key


def     lowercase		(  _lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase   ):
  UpperCAmelCase__         = pt_tuple_key[:-1] + ("""scale""",)
  if (
      any("""norm""" in str_ for str_ in pt_tuple_key   )
      and (pt_tuple_key[-1] == "bias")
      and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
      and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
  ):
    UpperCAmelCase__         = pt_tuple_key[:-1] + ("""scale""",)
    return renamed_pt_tuple_key, pt_tensor
  elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
    UpperCAmelCase__         = pt_tuple_key[:-1] + ("""scale""",)
    return renamed_pt_tuple_key, pt_tensor

  # embedding
  if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
    UpperCAmelCase__         = pt_tuple_key[:-1] + ("""embedding""",)
    return renamed_pt_tuple_key, pt_tensor

  # conv layer
  UpperCAmelCase__         = pt_tuple_key[:-1] + ("""kernel""",)
  if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
    UpperCAmelCase__         = pt_tensor.transpose(2 ,  3 ,  1 ,  0   )
    return renamed_pt_tuple_key, pt_tensor

  # linear layer
  UpperCAmelCase__         = pt_tuple_key[:-1] + ("""kernel""",)
  if pt_tuple_key[-1] == "weight":
    UpperCAmelCase__         = pt_tensor.T
    return renamed_pt_tuple_key, pt_tensor

  # old PyTorch layer norm weight
  UpperCAmelCase__         = pt_tuple_key[:-1] + ("""weight""",)
  if pt_tuple_key[-1] == "gamma":
    return renamed_pt_tuple_key, pt_tensor

  # old PyTorch layer norm bias
  UpperCAmelCase__         = pt_tuple_key[:-1] + ("""bias""",)
  if pt_tuple_key[-1] == "beta":
    return renamed_pt_tuple_key, pt_tensor

  return pt_tuple_key, pt_tensor





def     lowercase		(  _lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase=42   ):
  # Step 1: Convert pytorch tensor to numpy
  UpperCAmelCase__         = {k: v.numpy() for k, v in pt_state_dict.items()}

  # Step 2: Since the model is stateless, get random Flax params
  UpperCAmelCase__         = flax_model.init_weights(PRNGKey(UpperCamelCase__   )   )

  UpperCAmelCase__         = flatten_dict(UpperCamelCase__   )
  UpperCAmelCase__         = {}

  # Need to change some parameters name to match Flax names
  for pt_key, pt_tensor in pt_state_dict.items():
    UpperCAmelCase__         = rename_key(UpperCamelCase__   )
    UpperCAmelCase__         = tuple(renamed_pt_key.split("""."""   )   )

    # Correctly rename weight parameters
    UpperCAmelCase__    ,       UpperCAmelCase__         = rename_key_and_reshape_tensor(UpperCamelCase__ ,  UpperCamelCase__ ,  UpperCamelCase__   )

    if flax_key in random_flax_state_dict:
      if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
        raise ValueError(
            F'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
            F'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''   )

        # also add unexpected weight so that warning is thrown
    UpperCAmelCase__         = jnp.asarray(UpperCamelCase__   )

  return unflatten_dict(UpperCamelCase__   )





'''simple docstring'''




import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       List[str]:
    def wrapper(*UpperCamelCase__							,    **UpperCamelCase__							):
        __lowerCamelCase            =    timeit.default_timer()
        __lowerCamelCase            =    func(*UpperCamelCase__							,    **UpperCamelCase__							)
        __lowerCamelCase            =    timeit.default_timer() - starttime
        return delta

    __lowerCamelCase            =    func.__name__

    return wrapper






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__=1_00							,    UpperCamelCase__=None							)      ->       Dict:
    __lowerCamelCase            =    []
    __lowerCamelCase            =    seq_shapes or {}
    for i in range(UpperCamelCase__							):
        __lowerCamelCase            =    {}
        for col_id, (k, v) in enumerate(features.items()							):
            if isinstance(UpperCamelCase__							,    _ArrayXD							):
                __lowerCamelCase            =    np.random.rand(*v.shape							).astype(v.dtype							)
            elif isinstance(UpperCamelCase__							,    datasets.Value							):
                if v.dtype == "string":
                    __lowerCamelCase            =    '''The small grey turtle was surprisingly fast when challenged.'''
                else:
                    __lowerCamelCase            =    np.random.randint(10							,    size=1							).astype(v.dtype							).item()
            elif isinstance(UpperCamelCase__							,    datasets.Sequence							):
                while isinstance(UpperCamelCase__							,    datasets.Sequence							):
                    __lowerCamelCase            =    v.feature
                __lowerCamelCase            =    seq_shapes[k]
                __lowerCamelCase            =    np.random.rand(*UpperCamelCase__							).astype(v.dtype							)
            __lowerCamelCase            =    data

        dummy_data.append((i, example)							)

    return dummy_data






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__=1_00							,    UpperCamelCase__=None							)      ->       Dict:
    __lowerCamelCase            =    generate_examples(UpperCamelCase__							,    num_examples=UpperCamelCase__							,    seq_shapes=UpperCamelCase__							)

    with ArrowWriter(features=UpperCamelCase__							,    path=UpperCamelCase__							) as writer:
        for key, record in dummy_data:
            __lowerCamelCase            =    features.encode_example(UpperCamelCase__							)
            writer.write(UpperCamelCase__							)

        __lowerCamelCase      ,    __lowerCamelCase            =    writer.finalize()

    if not num_final_examples == num_examples:
        raise ValueError(
            f"""Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."""							)

    __lowerCamelCase            =    datasets.Dataset.from_file(filename=UpperCamelCase__							,    info=datasets.DatasetInfo(features=UpperCamelCase__							)							)

    return dataset
